Cover Image

PAPERBACK
$66.00



View/Hide Left Panel
Click for next page ( 72


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 71
PART IV Methoclological Issues and Work in Progress

OCR for page 71

OCR for page 71
Use of Large Data Bases: Introduction Emmet'B. Keeler, Session Moderator i Although it is not entirely clear what is meant by large data bases, we know that to administer its programs, the Health Care Financing Administration (HCFA) collects enormous amounts of data that contain information on the location and use of medical services, both inpatient and outpatient, and information on everyone covered by and mortality associated with Medicare and Medicaid. To keep the costs of administration down, HCFA does not collect all the clinical detail that researchers might want. However, the data are fairly universal in scope, and there are lots of possibilities for using them as a resource: linking them to outside data, putting together different HCFA files (such as hospital records with outpatient records), and so forth. Used creatively, they are an invaluable resource for any- body interested in studying what is actually occurring in the United States. Janet B. Mitchell is president of the Center for Health Economics Re- search in Needham, Massachusetts. She and her institute are both well known for their studies of payment mechanisms and their effects on physi- cians. Dr. Mitchell gives a general methodological overview of the things that can be done with administrative data sets. Elliot S. Fisher is a physician at Dartmouth Medical School and was evolved in the large data set analysis of the Wennberg study, which is the prototype for effectiveness research. (John Wennberg is director of the Center for Evaluative Clinical Sciences.) Dr. Fisher and Dr. Wennberg highlight the problems and achievements of the original study and describe the use of administrative data in the ongoing assessment of treatments for benign pro static hyperplasia. Stephen F. Jencks is a physician and chief scientist at the Office of Research in HCFA. Dr. Jencks has extensive experience in sponsoring, critiquing, and performing a number of studies looking at postadmission mortality. He discusses the uses and limitations of claims data for out- comes research. 73

OCR for page 71
1 ~ The Role of Large Data Bases in Effectiveness Research Janet B. Mitchell The first question in any consideration of the use of large data bases in effectiveness research is: what is a "large data base"? Usually, it refers to administrative records, or insurance claims data, regarding patients receiving various treatments. The nice thing about using claims for research purposes is that someone else actually collects the data, namely, providers filling out the claims forms. By the time the researcher receives the claims, the data are already computerized in a consistent format. SIZE OF LARGE DATA BASES One of the major difficulties in working with these data bases is that they are indeed large-enormous or gargantuan might be more appropriate descriptors! It is not uncommon to work with millions of claims on hundreds of reels of tape. I am sure many of you have conducted clinical research involving hundreds of patients, and you may be wondering why I or anyone else would want to get involved with millions of records in the first place. The reason, of course, is that these records do not represent individual patients, but rather pieces of information describing the medical services received by each patient. These pieces of information need to be put together in order to obtain a picture of an episode of care. During a single inpatient episode, for example, a patient might incur anywhere from a dozen to a hundred bills. For longer periods of care, the number of records would be consider- ably larger, especially for sicker patients. Why so many claims? In Medicare, for instance, inpatient hospital and skilled nursing facility stays are billed using a single claim, but physician and other Part B services are billed individually. Thus, there will be a claim for every discrete service: for every surgical procedure, for every 74

OCR for page 71
USE OF LARGE DATA BASES 75 visit, for every X-ray, for every laboratory test. The detailed nature of these claims data bases is one of their greatest strengths; the creative researcher can use them in an almost infinite variety of ways. USES OF LARGE DATA BASES Probably the most common use of claims data for effectiveness research is to follow patients with a specific diagnosis or patients receiving a specific therapy. Diagnoses are available on institutional claims; procedures are documented on all physician bills. For example: What happens to patients receiving percutaneous transluminal angioplasty? What services do those patients receive afterwards and in what kinds of settings? Some services will suggest that complications have arisen, say, if the procedure is followed closely by repeat angioplasty or bypass surgery. Outcomes, such as readmission and mortality rates, can also be studied. Besides studying individual patients or episodes of care, claims data can also be used to evaluate effectiveness at the level of individual providers, such as hospitals. Thus they provide an opportunity to examine questions such as whether mortality rates for a given procedure depend in part on a hospital's surgical volume, for example. MEDICARE DATA BASES Medicare claims files are particularly valuable, for several reasons. First, every beneficiary has a unique identification number based on his or her Social Security number. Because this number is attached to every Part A and Part B claim, it is easy to construct episodes of care for individual patients. Sometimes, however, these numbers are slightly different on the Part A and the Part B claims. Fortunately, there are fairly straightforward algorithms that can be used to equate them. Second, the Health Care Financing Administration (HCFA) maintains claims data on samples of patients for research purposes. These samples are selected, based on their identification numbers, and remain in the data base until the patient dies. This enables researchers to follow the same patients over a period of years. In addition, HCFA maintains eligibility files that include information on dates of death. Because of the need to prevent Social Security checks from being mailed to deceased beneficiaries, these deaths are verified and the dates are believed to be reasonably valid. Historically, researchers have primarily used Part A hospital records to study effectiveness issues. Only relatively recently have they discovered the value of Part B claims, either in their own right or as supplements to Part A data. One major limitation of hospital claims for effectiveness research is the absence of detailed information on what was actually done to the

OCR for page 71
76 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE patient in the hospital. Part A claims do include information on surgical procedures, but this information is generally limited to procedures that affect assignment to diagnosis-related groups (DRGs); thus, many diagnostic sur- geries are missing. The only data available on ancillary diagnostic tests, furthermore, are simply charges per revenue center, that is, charges for radiology with no indication of how many X-rays were performed or which ones. There is also no information on physician visits and consultations. Except for some services performed by residents, however, every physi- cian service will show up as a Part B bill. These bills provide the researcher with an in-depth look at the mix of services provided during the hospital stay. Because each physician bill includes the date of service, we can also look at the timing of various tests. This can be useful in trying to infer the clinical decision-making process that took place during the hospitalization. The Part B detail can also be used to define the universe of patients receiving a specific therapy of interest. Not all patients undergoing coronary bypass surgery will be identified through DRGs 106 and 107, for example; a surprising number will show up in other DRGs, such as those involving valve replacements. This is important, as geographic variation has been found in the frequency with which bypass operations are combined with other open-heart surgery. Thus, how a study sample is selected could have profound effects on the research findings. Anesthesiologists and assistant surgeons frequently report a different procedure than that billed by the surgeon. Usually, they are reporting an operation in the same general anatomic area, but not always. My rule has always been to assume that the primary surgeon is right and use what this surgeon reports to define the sample. Using claims data to examine outcomes associated with ambulatory epi- sodes of care is more problematic because of the absence of diagnostic information on the Part B claims. Thus it is not possible to determine the reason for a given office visit or to trace referral patterns accurately. Beginning this year, however, physicians are being required to assign diagnoses a code number from the International Classification of Diseases (ICD-9-CM) and to include those numbers on their claims, so it is possible that such analyses will be feasible in the future. It is possible to identify specific illnesses indirectly, using the procedure codes on the Part B claims. Services provided under Medicare Part B are billed using the Common Procedural Terminology (CPT-4) or, in the case of nonphysician services, a system developed by HCFA known as HCPCS (HCFA Common Procedure Coding System). There are over 10,000 codes available for billing purposes. This wealth of codes is the despair of many policymakers, who feel it helps fuel the inflation in physician spending. However, it is a boon to researchers. Unlike the ICD-9-CM procedure codes, which are often vague concerning

OCR for page 71
USE OF LARGE DATA BASES 77 the precise nature of the surgical procedure or diagnostic test, CPT-4 records that information in excruciating detail. We can tell, for example, not just that a patient received a total hip replacement, but whether it was an origi- nal replacement, whether it was a conversion of previous hip surgery to a total hip replacement, or whether it was a revision of an earlier replace- ment. In the latter instance, we also know whether the revision involved the acetabular part of the hip, the femoral component, or both. Some examples of identifying outpatient treatments through the procedure codes would include hemodialysis for end-stage renal disease patients and chemotherapy for can- cer patients. A particular interest of many researchers is how the utilization of services varies around the country. Unfortunately, only the institutional claims include information on exactly where the service was provided. The only geographic identifiers on Part B claims are the carrier (which generally corresponds to a state) and the reasonable charge locality. The reasonable charge locality is a fairly arbitrary geographic entity used by the carriers to determine allowed charges. It provides a finer breakdown than the state, but it is still fairly crude. In fact, for 16 states, only a single statewide locality is used. The Part B claims also lack any information on where the patient lives. This means that population-based measures of utilization and outcomes can be easily created only for hospital services. The researcher who wants to study the utilization of ambulatory services must obtain information on the patient's residence from HCFA's eligibility files and merge it. Let me mention here an additional consideration when analyzing Part B claims data. Although Medicare is a national program, each carrier has considerable flexibility in how it actually processes and pays claims. These idiosyncrasies can lead the unwary researcher astray. Permanent pacemaker insertion is a good example of the potential prob- lems that can be encountered. A number of physicians use the team approach to pacemaker insertion; a surgeon makes the pocket to hold the device and a cardiologist inserts the electrodes. Carriers have attempted to recognize the team approach and reimburse it in a number of different ways. In some states, each physician submits a bill for pacemaker insertion without any indication that another physician was involved. The carrier knows which physicians practice in this way and pays each physician less than if he or she had performed the procedure independently. The researcher cannot tell this from the claims data, however, and it will appear as if twice the number of pacemakers were inserted in that area. One carrier has dealt with the team approach by having one physician bill for the insertion, while the other physician bills for pacemaker repair. If a researcher did not know this ahead of time, it would appear that there were a lot of pacemaker failures in that particular state. So far, I have been talking about Part B physician and Part A hospital

OCR for page 71
78 EFFECTIVENESS AlID OUTCOMES IN HEALTlI CARE claims, but Medicare claims are also available for other types of services, such as skilled nursing facility and home health care. These claims can be particularly valuable for examining rehabilitative treatment; one example might be to look at the care received following hip fracture. MEDICAID DATA BASES To date, most research has focused on Medicare patients, for two reasons. The Medicare program is consuming an increasingly large share of the federal budget, and the claims data have been readily available (more or less) from HCFA. Because of problems in data acquisition, the services received by Medicaid patients have historically received less attention. HCFA is working on some new data bases that will eventually provide Medicaid claims in a consistent format for all states. I believe data from about a half- dozen states are available at the present time. There are several advantages in using Medicaid claims to analyze effec- tiveness, either in conjunction with or in place of Medicare claims. For one, the Medicaid-eligible population encompasses a much wider age range, thus permitting study of pregnancy and pediatric illnesses. In addition, there are other important conditions whose incidence is simply not sufficient to study in the Medicare population. Substance abuse is one example; another is AIDS. Although the permanently and totally disabled are also eligible for Medicare coverage, most AIDS patients simply do not survive long enough to qualify for benefits. A large number do become eligible for Medicaid, often early in the disease process, and Medicaid claims can be used to help track the effectiveness of various treatment regimens. Another advantage of Medicaid claims is that the Medicaid program covers a wider range of benefits than does Medicare, especially in the areas of long-term care and prescription drugs. A major disadvantage of Medicare claims has been that, although the program serves the elderly, it covers only a small part of long-term care only 150 days of nursing home care per year, and that care must be in a skilled nursing facility. This means that studies of patients with chronic conditions requiring ongoing custodial care (for example, Alzheimer's disease, stroke, or spinal cord injury) will be able to paint only a partial picture of health care use. Because state Medicaid programs do cover these services, however, Medicaid claims can be used to fill some important gaps. Similarly, because Medicaid pays for most prescription drugs, these claims can be used to evaluate alternative treatments or to identify a sample of patients undergoing a given treatment regimen: for example, all AIDS patients receiving AZT. Data on prescription drugs can be used in many ways. An obvious one is to compare the effectiveness of drug therapy to surgical intervention. Another is to look at adverse or unintended consequences of

OCR for page 71
USE OF LARGE DATA BASES 79 specific medications. One researcher, for example, examined the incidence of hip fracture in patients receiving psychotropic drugs. One of the main disadvantages of Medicaid claims is that Medicaid recipients are not representative of the population at large. This is in contrast to Medicare recipients: a sample of Medicare patients with myocardial infarction is virtually synonymous with a sample of elderly persons with myocardial infarction. Another disadvantage is that, unlike Medicare beneficiaries, Medicaid patients are not always continuously eligible for care. This is particularly true of recipients of Aid to Families with Dependent Children, who may be eligible for only some months in a year. The Medicare Catastrophic Coverage Act passed by Congress last year would have given Medicare many of Medicaid's data advantages, and thus research advantages, by expanding coverage. Both the skilled nursing facility benefit and the home health care benefit were extended, for example, providing more data on these components of postacute care. Screening mammography was a brand-new benefit. Most important, the legislation expanded Medicare coverage to outpatient prescription drugs. Repeal of the Act in late 1989 deprived researchers of the opportunity to broaden the questions that could be addressed using Medicare claims data and thus expand effectiveness and outcomes research.